function [ label_predict, label_output ] = my_LEAD_Get_Prob2( test_data, idx, classifiers )

n_examples = size(test_data, 1);
n_labels = length(idx);
label_predict = zeros(n_examples, n_labels);
label_output = label_predict;
for i_label = idx
    parent = classifiers{1,i_label}.parent;
    testing_instance_matrix = [test_data, label_predict(:, parent)];
    testing_label_vector = ones(n_examples,1);
    [label_predict(:, i_label), ~, raw_output] = lsvmpredict( ...
        testing_label_vector, sparse(testing_instance_matrix), ...
        classifiers{1, i_label}.model);
    if label_predict(1, i_label)*raw_output(1) < 0
        raw_output = -raw_output;
    end
    label_output(:, i_label) = sigmoid(raw_output);
end

end
